if (!require("remotes"))
install.packages("remotes")
remotes::install_github("flavjack/inti")ESTIMULANTES EN LA GERMINACIÓN Y BIOMETRÍA INICIAL DE DOS VARIEDADES DE MAÍZ MORADO (Zea mays L.)
1 Setup
Instalar version en desarrollo.
library(emmeans)
library(corrplot)
library(multcomp)
source('https://inkaverse.com/setup.r')
session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.4.1 (2024-06-14 ucrt)
os Windows 11 x64 (build 22631)
system x86_64, mingw32
ui RTerm
language (EN)
collate Spanish_Latin America.utf8
ctype Spanish_Latin America.utf8
tz America/Lima
date 2024-07-25
pandoc 3.1.11 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
agricolae 1.3-7 2023-10-22 [1] CRAN (R 4.4.0)
AlgDesign 1.2.1 2022-05-25 [1] CRAN (R 4.4.0)
askpass 1.2.0 2023-09-03 [1] CRAN (R 4.4.0)
boot 1.3-30 2024-02-26 [2] CRAN (R 4.4.1)
cachem 1.1.0 2024-05-16 [1] CRAN (R 4.4.0)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.4.0)
cli 3.6.3 2024-06-21 [1] CRAN (R 4.4.1)
cluster 2.1.6 2023-12-01 [2] CRAN (R 4.4.1)
coda 0.19-4.1 2024-01-31 [1] CRAN (R 4.4.0)
codetools 0.2-20 2024-03-31 [2] CRAN (R 4.4.1)
colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.4.0)
corrplot * 0.92 2021-11-18 [1] CRAN (R 4.4.1)
cowplot * 1.1.3 2024-01-22 [1] CRAN (R 4.4.0)
curl 5.2.1 2024-03-01 [1] CRAN (R 4.4.0)
devtools * 2.4.5 2022-10-11 [1] CRAN (R 4.4.0)
digest 0.6.36 2024-06-23 [1] CRAN (R 4.4.1)
dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.4.0)
DT 0.33 2024-04-04 [1] CRAN (R 4.4.0)
ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.4.0)
emmeans * 1.10.3 2024-07-01 [1] CRAN (R 4.4.1)
estimability 1.5.1 2024-05-12 [1] CRAN (R 4.4.0)
evaluate 0.24.0 2024-06-10 [1] CRAN (R 4.4.0)
FactoMineR * 2.11 2024-04-20 [1] CRAN (R 4.4.0)
fansi 1.0.6 2023-12-08 [1] CRAN (R 4.4.0)
fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.0)
flashClust 1.01-2 2012-08-21 [1] CRAN (R 4.4.0)
forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.4.0)
fs 1.6.4 2024-04-25 [1] CRAN (R 4.4.0)
gargle 1.5.2 2023-07-20 [1] CRAN (R 4.4.0)
generics 0.1.3 2022-07-05 [1] CRAN (R 4.4.0)
ggplot2 * 3.5.1 2024-04-23 [1] CRAN (R 4.4.0)
ggrepel 0.9.5 2024-01-10 [1] CRAN (R 4.4.0)
glue 1.7.0 2024-01-09 [1] CRAN (R 4.4.0)
googledrive * 2.1.1 2023-06-11 [1] CRAN (R 4.4.0)
googlesheets4 * 1.1.1 2023-06-11 [1] CRAN (R 4.4.0)
gsheet * 0.4.5 2020-04-07 [1] CRAN (R 4.4.1)
gtable 0.3.5 2024-04-22 [1] CRAN (R 4.4.0)
hms 1.1.3 2023-03-21 [1] CRAN (R 4.4.0)
htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.4.0)
htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.4.0)
httpuv 1.6.15 2024-03-26 [1] CRAN (R 4.4.0)
httr 1.4.7 2023-08-15 [1] CRAN (R 4.4.0)
huito * 0.2.4 2023-10-25 [1] CRAN (R 4.4.0)
inti * 0.6.5 2024-07-25 [1] local
jsonlite 1.8.8 2023-12-04 [1] CRAN (R 4.4.0)
knitr * 1.48 2024-07-07 [1] CRAN (R 4.4.1)
later 1.3.2 2023-12-06 [1] CRAN (R 4.4.0)
lattice 0.22-6 2024-03-20 [2] CRAN (R 4.4.1)
leaps 3.2 2024-06-10 [1] CRAN (R 4.4.0)
lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.4.0)
lme4 1.1-35.5 2024-07-03 [1] CRAN (R 4.4.1)
lubridate * 1.9.3 2023-09-27 [1] CRAN (R 4.4.0)
magick * 2.8.4 2024-07-14 [1] CRAN (R 4.4.1)
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.4.0)
MASS * 7.3-60.2 2024-04-26 [2] CRAN (R 4.4.1)
Matrix 1.7-0 2024-04-26 [2] CRAN (R 4.4.1)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.4.0)
mime 0.12 2021-09-28 [1] CRAN (R 4.4.0)
miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.4.0)
minqa 1.2.7 2024-05-20 [1] CRAN (R 4.4.0)
mnormt 2.1.1 2022-09-26 [1] CRAN (R 4.4.0)
multcomp * 1.4-26 2024-07-18 [1] CRAN (R 4.4.1)
multcompView 0.1-10 2024-03-08 [1] CRAN (R 4.4.0)
munsell 0.5.1 2024-04-01 [1] CRAN (R 4.4.0)
mvtnorm * 1.2-5 2024-05-21 [1] CRAN (R 4.4.0)
nlme 3.1-164 2023-11-27 [2] CRAN (R 4.4.1)
nloptr 2.1.1 2024-06-25 [1] CRAN (R 4.4.1)
openssl 2.2.0 2024-05-16 [1] CRAN (R 4.4.0)
pillar 1.9.0 2023-03-22 [1] CRAN (R 4.4.0)
pkgbuild 1.4.4 2024-03-17 [1] CRAN (R 4.4.0)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.0)
pkgload 1.4.0 2024-06-28 [1] CRAN (R 4.4.1)
profvis 0.3.8 2023-05-02 [1] CRAN (R 4.4.0)
promises 1.3.0 2024-04-05 [1] CRAN (R 4.4.0)
psych * 2.4.6.26 2024-06-27 [1] CRAN (R 4.4.1)
purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.4.0)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.4.0)
rappdirs 0.3.3 2021-01-31 [1] CRAN (R 4.4.0)
Rcpp 1.0.13 2024-07-17 [1] CRAN (R 4.4.1)
readr * 2.1.5 2024-01-10 [1] CRAN (R 4.4.0)
remotes 2.5.0 2024-03-17 [1] CRAN (R 4.4.0)
rlang 1.1.4 2024-06-04 [1] CRAN (R 4.4.0)
rmarkdown 2.27 2024-05-17 [1] CRAN (R 4.4.0)
rstudioapi 0.16.0 2024-03-24 [1] CRAN (R 4.4.0)
sandwich 3.1-0 2023-12-11 [1] CRAN (R 4.4.0)
scales 1.3.0 2023-11-28 [1] CRAN (R 4.4.0)
scatterplot3d 0.3-44 2023-05-05 [1] CRAN (R 4.4.0)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.4.0)
shiny * 1.8.1.1 2024-04-02 [1] CRAN (R 4.4.0)
showtext 0.9-7 2024-03-02 [1] CRAN (R 4.4.0)
showtextdb 3.0 2020-06-04 [1] CRAN (R 4.4.0)
stringi 1.8.4 2024-05-06 [1] CRAN (R 4.4.0)
stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.4.0)
survival * 3.6-4 2024-04-24 [2] CRAN (R 4.4.1)
sysfonts 0.8.9 2024-03-02 [1] CRAN (R 4.4.0)
TH.data * 1.1-2 2023-04-17 [1] CRAN (R 4.4.0)
tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.4.0)
tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.4.0)
tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.4.0)
tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.4.0)
timechange 0.3.0 2024-01-18 [1] CRAN (R 4.4.0)
tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.4.0)
urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.4.0)
usethis * 2.2.3 2024-02-19 [1] CRAN (R 4.4.0)
utf8 1.2.4 2023-10-22 [1] CRAN (R 4.4.0)
vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.4.0)
withr 3.0.0 2024-01-16 [1] CRAN (R 4.4.0)
xfun 0.46 2024-07-18 [1] CRAN (R 4.4.1)
xtable 1.8-4 2019-04-21 [1] CRAN (R 4.4.0)
yaml 2.3.9 2024-07-05 [1] CRAN (R 4.4.1)
zoo 1.8-12 2023-04-13 [1] CRAN (R 4.4.0)
[1] C:/Users/LENOVO/AppData/Local/R/win-library/4.4
[2] C:/Program Files/R/R-4.4.1/library
──────────────────────────────────────────────────────────────────────────────2 Refrencias
- (PCA) https://www.r-bloggers.com/2017/07/pca-course-using-factominer/
- (PCA) https://www.youtube.com/watch?v=Uhw-1NilmAk&ab_channel=Fran%C3%A7oisHusson
- (HCPC) https://youtu.be/EJqYTDTJJug
3 Import data
https://docs.google.com/spreadsheets/d/1E_l9uV3MT1qlJuVtWK66NgevqPH6fVJCekqNhS_VGm0/edit?gid=1893553741#gid=1893553741
url <- "https://docs.google.com/spreadsheets/d/1E_l9uV3MT1qlJuVtWK66NgevqPH6fVJCekqNhS_VGm0/edit?gid=1893553741#gid=1893553741"
gs <- url %>%
as_sheets_id()
imbibition <- gs %>%
range_read("imbibition") %>%
rename_with(~ tolower(.)) %>%
mutate(time = tiempo, .after = tiempo) %>%
mutate(across(1:tiempo, ~ as.factor(.)))
str(imbibition)
## tibble [2,100 × 7] (S3: tbl_df/tbl/data.frame)
## $ bloque : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ trat : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ tratamiento: Factor w/ 7 levels "Agua Destilada",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ variedad : Factor w/ 2 levels "criollo","Hibrido": 1 1 1 1 1 1 1 1 1 1 ...
## $ tiempo : Factor w/ 5 levels "0","3","6","9",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ time : num [1:2100] 0 0 0 0 0 0 0 0 0 0 ...
## $ peso : num [1:2100] 0.58 0.62 0.73 0.72 0.72 0.68 0.71 0.61 0.69 0.64 ...
germination <- gs %>%
range_read("germination") %>%
rename_with(~ tolower(.)) %>%
mutate(across(1:variedad, ~ as.factor(.)))
str(germination)
## tibble [42 × 10] (S3: tbl_df/tbl/data.frame)
## $ bloque : Factor w/ 3 levels "1","2","3": 1 2 3 1 2 3 1 2 3 1 ...
## $ tratamiento: Factor w/ 7 levels "Agua Destilada",..: 1 1 1 2 2 2 3 3 3 4 ...
## $ variedad : Factor w/ 2 levels "criollo","Hibrido": 1 1 1 1 1 1 1 1 1 1 ...
## $ dia 1 : num [1:42] 2 4 3 0 1 1 0 0 1 0 ...
## $ dia 2 : num [1:42] 5 4 5 3 2 1 1 4 5 1 ...
## $ dia 3 : num [1:42] 1 1 1 1 0 0 0 0 0 0 ...
## $ total : num [1:42] 8 9 9 4 3 2 1 4 6 1 ...
## $ pg : num [1:42] 80 90 90 40 30 20 10 40 60 10 ...
## $ vg : num [1:42] 2.67 3 3 2 1.5 ...
## $ ig : num [1:42] 2.4 2.7 2.7 0.8 0.6 0.4 0.1 0.4 1.2 0.1 ...
plantula <- gs %>%
range_read("plantula") %>%
rename_with(~ tolower(.)) %>%
mutate(across(1:variedad, ~ as.factor(.)))
str(plantula)
## tibble [210 × 16] (S3: tbl_df/tbl/data.frame)
## $ t : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ tratamiento : Factor w/ 7 levels "Agua Destilada",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ variedad : Factor w/ 2 levels "criollo","hibrido": 1 1 1 1 1 1 1 1 1 1 ...
## $ raiz_lgtd : num [1:210] 11 8 12 11 8 13 10 12 9 13 ...
## $ gsr_raiz : num [1:210] 1.3 1.19 1.51 1.21 1.17 1.13 1.68 1.27 1.03 1.16 ...
## $ num_raiz : num [1:210] 8 11 11 9 12 16 10 9 16 11 ...
## $ peso_fres_raiz : num [1:210] 4.82 3.21 4.91 4.42 4.62 6.07 4.97 6.13 3.05 4 ...
## $ peso_seco_raiz : num [1:210] 0.73 0.41 0.62 0.66 0.72 0.54 0.75 0.56 0.57 0.74 ...
## $ alt_planta : num [1:210] 30 26 28 32 25 27 28 35 29 29 ...
## $ gsr_tallo : num [1:210] 5.86 4.56 6.59 4.63 4.55 4.14 4.02 4.32 3.45 3.61 ...
## $ nhp_hoja : num [1:210] 5 5 5 6 4 5 5 5 5 5 ...
## $ larg_hoja : num [1:210] 26 23 21 27 29 22 24 30 25 23 ...
## $ grs_hoja : num [1:210] 0.94 1.15 0.89 0.98 1.01 0.72 0.62 1.03 0.71 1.34 ...
## $ anch_hoja : num [1:210] 19.3 19.9 21.5 17.3 18.9 ...
## $ peso_fres_brote: num [1:210] 5.34 5.99 5.45 4.81 7.03 6.79 4.99 4.53 3.56 4 ...
## $ peso_seco_brote: num [1:210] 0.5 0.49 1.04 0.78 0.68 0.67 0.69 0.78 0.73 0.75 ...4 Data summary
sm <- imbibition %>%
group_by(tratamiento, variedad, tiempo) %>%
summarise(across(peso, ~ sum(!is.na(.))))
sm
## # A tibble: 70 × 4
## # Groups: tratamiento, variedad [14]
## tratamiento variedad tiempo peso
## <fct> <fct> <fct> <int>
## 1 Agua Destilada criollo 0 30
## 2 Agua Destilada criollo 3 30
## 3 Agua Destilada criollo 6 30
## 4 Agua Destilada criollo 9 30
## 5 Agua Destilada criollo 12 30
## 6 Agua Destilada Hibrido 0 30
## 7 Agua Destilada Hibrido 3 30
## 8 Agua Destilada Hibrido 6 30
## 9 Agua Destilada Hibrido 9 30
## 10 Agua Destilada Hibrido 12 30
## # ℹ 60 more rows
sm <- germination %>%
group_by(tratamiento, variedad) %>%
summarise(across(pg:ig, ~ sum(!is.na(.))))
sm
## # A tibble: 14 × 5
## # Groups: tratamiento [7]
## tratamiento variedad pg vg ig
## <fct> <fct> <int> <int> <int>
## 1 Agua Destilada criollo 3 3 3
## 2 Agua Destilada Hibrido 3 3 3
## 3 Algas Marinas 1 L/cil criollo 3 3 3
## 4 Algas Marinas 1 L/cil Hibrido 3 3 3
## 5 Algas Marinas 1,5 L/cil criollo 3 3 3
## 6 Algas Marinas 1,5 L/cil Hibrido 3 3 3
## 7 Azufre 100 gr.200 L-1 criollo 3 3 3
## 8 Azufre 100 gr.200 L-1 Hibrido 3 3 3
## 9 Azufre 150 gr.200 L-1 criollo 3 3 3
## 10 Azufre 150 gr.200 L-1 Hibrido 3 3 3
## 11 Suero de leche 10% criollo 3 3 3
## 12 Suero de leche 10% Hibrido 3 3 3
## 13 Suero de leche 30% criollo 3 3 3
## 14 Suero de leche 30% Hibrido 3 3 3
sm <- plantula %>%
group_by(tratamiento, variedad) %>%
summarise(across(where(is.numeric), ~ sum(!is.na(.))))
sm
## # A tibble: 14 × 15
## # Groups: tratamiento [7]
## tratamiento variedad raiz_lgtd gsr_raiz num_raiz peso_fres_raiz
## <fct> <fct> <int> <int> <int> <int>
## 1 Agua Destilada criollo 15 15 15 15
## 2 Agua Destilada hibrido 15 15 15 15
## 3 Algas Marinas 1 L/cil criollo 15 15 15 15
## 4 Algas Marinas 1 L/cil hibrido 15 15 15 15
## 5 Algas Marinas 1,5 L/cil criollo 15 15 15 15
## 6 Algas Marinas 1,5 L/cil hibrido 15 15 15 15
## 7 Azufre 100 gr.200 L-1 criollo 15 15 15 15
## 8 Azufre 100 gr.200 L-1 hibrido 15 15 15 15
## 9 Azufre 150 gr.200 L-1 criollo 15 15 15 15
## 10 Azufre 150 gr.200 L-1 hibrido 15 15 15 15
## 11 Suero de leche 10% criollo 15 15 15 15
## 12 Suero de leche 10% hibrido 15 15 15 15
## 13 Suero de leche 30% criollo 15 15 15 15
## 14 Suero de leche 30% hibrido 15 15 15 15
## # ℹ 9 more variables: peso_seco_raiz <int>, alt_planta <int>, gsr_tallo <int>,
## # nhp_hoja <int>, larg_hoja <int>, grs_hoja <int>, anch_hoja <int>,
## # peso_fres_brote <int>, peso_seco_brote <int>5 Objetivos
Evaluar los parámetros de germinación de dos variedades de semillas de maiz morado usando bioestimulante orgánico.
Identificar el mejor tratamiento que influye positivamente en el crecimiento y desarrollo de plantulas en el cultivo de Maíz morado.
5.1 Objetivo Específico 1
Evaluar los parámetros de germinación de dos variedades de semillas de maiz morado usando bioestimulante orgánico.
- Imbibiciación, % germinación, velocidad e IG
5.1.1 Imbibición
trait <- "peso"
fb <- imbibition
lmm <- paste({{trait}}, "~ 1 + (1|bloque) + tratamiento*variedad + (1 + tiempo|trat)") %>% as.formula()
lmd <- paste({{trait}}, "~ bloque + tiempo + tratamiento*variedad") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## [1] index bloque tratamiento variedad tiempo trat
## [7] peso resi res_MAD rawp.BHStud adjp bholm
## [13] out_flag
## <0 rows> (o 0- extensión row.names)
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: peso
## Df Sum Sq Mean Sq F value Pr(>F)
## bloque 2 0.0021 0.00105 0.1222 0.885
## tiempo 4 10.0058 2.50146 289.7715 <0.0000000000000002 ***
## tratamiento 6 3.2174 0.53624 62.1186 <0.0000000000000002 ***
## variedad 1 0.6165 0.61649 71.4150 <0.0000000000000002 ***
## tratamiento:variedad 6 2.6467 0.44111 51.0987 <0.0000000000000002 ***
## Residuals 2080 17.9556 0.00863
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ tiempo|variedad|tratamiento) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| tiempo | variedad | tratamiento | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 12 | criollo | Agua Destilada | 0.8296986 | 0.0086019 | 2080 | 0.8128293 | 0.8465678 | a |
| 3 | 9 | criollo | Agua Destilada | 0.8279129 | 0.0086019 | 2080 | 0.8110436 | 0.8447821 | a |
| 2 | 3 | criollo | Agua Destilada | 0.7645081 | 0.0086019 | 2080 | 0.7476388 | 0.7813774 | b |
| 4 | 6 | criollo | Agua Destilada | 0.7620295 | 0.0086019 | 2080 | 0.7451603 | 0.7788988 | b |
| 5 | 0 | criollo | Agua Destilada | 0.6398176 | 0.0086019 | 2080 | 0.6229483 | 0.6566869 | c |
| 11 | 12 | criollo | Algas Marinas 1 L/cil | 0.7157719 | 0.0086019 | 2080 | 0.6989026 | 0.7326412 | a |
| 13 | 9 | criollo | Algas Marinas 1 L/cil | 0.7139862 | 0.0086019 | 2080 | 0.6971169 | 0.7308555 | a |
| 12 | 3 | criollo | Algas Marinas 1 L/cil | 0.6505814 | 0.0086019 | 2080 | 0.6337122 | 0.6674507 | b |
| 14 | 6 | criollo | Algas Marinas 1 L/cil | 0.6481029 | 0.0086019 | 2080 | 0.6312336 | 0.6649721 | b |
| 15 | 0 | criollo | Algas Marinas 1 L/cil | 0.5258910 | 0.0086019 | 2080 | 0.5090217 | 0.5427602 | c |
| 21 | 12 | criollo | Algas Marinas 1,5 L/cil | 0.6749719 | 0.0086019 | 2080 | 0.6581026 | 0.6918412 | a |
| 23 | 9 | criollo | Algas Marinas 1,5 L/cil | 0.6731862 | 0.0086019 | 2080 | 0.6563169 | 0.6900555 | a |
| 22 | 3 | criollo | Algas Marinas 1,5 L/cil | 0.6097814 | 0.0086019 | 2080 | 0.5929122 | 0.6266507 | b |
| 24 | 6 | criollo | Algas Marinas 1,5 L/cil | 0.6073029 | 0.0086019 | 2080 | 0.5904336 | 0.6241721 | b |
| 25 | 0 | criollo | Algas Marinas 1,5 L/cil | 0.4850910 | 0.0086019 | 2080 | 0.4682217 | 0.5019602 | c |
| 31 | 12 | criollo | Azufre 100 gr.200 L-1 | 0.6591052 | 0.0086019 | 2080 | 0.6422360 | 0.6759745 | a |
| 33 | 9 | criollo | Azufre 100 gr.200 L-1 | 0.6573195 | 0.0086019 | 2080 | 0.6404503 | 0.6741888 | a |
| 32 | 3 | criollo | Azufre 100 gr.200 L-1 | 0.5939148 | 0.0086019 | 2080 | 0.5770455 | 0.6107840 | b |
| 34 | 6 | criollo | Azufre 100 gr.200 L-1 | 0.5914362 | 0.0086019 | 2080 | 0.5745669 | 0.6083055 | b |
| 35 | 0 | criollo | Azufre 100 gr.200 L-1 | 0.4692243 | 0.0086019 | 2080 | 0.4523550 | 0.4860936 | c |
| 41 | 12 | criollo | Azufre 150 gr.200 L-1 | 0.6322386 | 0.0086019 | 2080 | 0.6153693 | 0.6491078 | a |
| 43 | 9 | criollo | Azufre 150 gr.200 L-1 | 0.6304529 | 0.0086019 | 2080 | 0.6135836 | 0.6473221 | a |
| 42 | 3 | criollo | Azufre 150 gr.200 L-1 | 0.5670481 | 0.0086019 | 2080 | 0.5501788 | 0.5839174 | b |
| 44 | 6 | criollo | Azufre 150 gr.200 L-1 | 0.5645695 | 0.0086019 | 2080 | 0.5477003 | 0.5814388 | b |
| 45 | 0 | criollo | Azufre 150 gr.200 L-1 | 0.4423576 | 0.0086019 | 2080 | 0.4254883 | 0.4592269 | c |
| 51 | 12 | criollo | Suero de leche 10% | 0.8092386 | 0.0086019 | 2080 | 0.7923693 | 0.8261078 | a |
| 53 | 9 | criollo | Suero de leche 10% | 0.8074529 | 0.0086019 | 2080 | 0.7905836 | 0.8243221 | a |
| 52 | 3 | criollo | Suero de leche 10% | 0.7440481 | 0.0086019 | 2080 | 0.7271788 | 0.7609174 | b |
| 54 | 6 | criollo | Suero de leche 10% | 0.7415695 | 0.0086019 | 2080 | 0.7247003 | 0.7584388 | b |
| 55 | 0 | criollo | Suero de leche 10% | 0.6193576 | 0.0086019 | 2080 | 0.6024883 | 0.6362269 | c |
| 61 | 12 | criollo | Suero de leche 30% | 0.7740386 | 0.0086019 | 2080 | 0.7571693 | 0.7909078 | a |
| 63 | 9 | criollo | Suero de leche 30% | 0.7722529 | 0.0086019 | 2080 | 0.7553836 | 0.7891221 | a |
| 62 | 3 | criollo | Suero de leche 30% | 0.7088481 | 0.0086019 | 2080 | 0.6919788 | 0.7257174 | b |
| 64 | 6 | criollo | Suero de leche 30% | 0.7063695 | 0.0086019 | 2080 | 0.6895003 | 0.7232388 | b |
| 65 | 0 | criollo | Suero de leche 30% | 0.5841576 | 0.0086019 | 2080 | 0.5672883 | 0.6010269 | c |
| 6 | 12 | Hibrido | Agua Destilada | 0.7764386 | 0.0086019 | 2080 | 0.7595693 | 0.7933078 | a |
| 8 | 9 | Hibrido | Agua Destilada | 0.7746529 | 0.0086019 | 2080 | 0.7577836 | 0.7915221 | a |
| 7 | 3 | Hibrido | Agua Destilada | 0.7112481 | 0.0086019 | 2080 | 0.6943788 | 0.7281174 | b |
| 9 | 6 | Hibrido | Agua Destilada | 0.7087695 | 0.0086019 | 2080 | 0.6919003 | 0.7256388 | b |
| 10 | 0 | Hibrido | Agua Destilada | 0.5865576 | 0.0086019 | 2080 | 0.5696883 | 0.6034269 | c |
| 16 | 12 | Hibrido | Algas Marinas 1 L/cil | 0.7279719 | 0.0086019 | 2080 | 0.7111026 | 0.7448412 | a |
| 18 | 9 | Hibrido | Algas Marinas 1 L/cil | 0.7261862 | 0.0086019 | 2080 | 0.7093169 | 0.7430555 | a |
| 17 | 3 | Hibrido | Algas Marinas 1 L/cil | 0.6627814 | 0.0086019 | 2080 | 0.6459122 | 0.6796507 | b |
| 19 | 6 | Hibrido | Algas Marinas 1 L/cil | 0.6603029 | 0.0086019 | 2080 | 0.6434336 | 0.6771721 | b |
| 20 | 0 | Hibrido | Algas Marinas 1 L/cil | 0.5380910 | 0.0086019 | 2080 | 0.5212217 | 0.5549602 | c |
| 26 | 12 | Hibrido | Algas Marinas 1,5 L/cil | 0.7881052 | 0.0086019 | 2080 | 0.7712360 | 0.8049745 | a |
| 28 | 9 | Hibrido | Algas Marinas 1,5 L/cil | 0.7863195 | 0.0086019 | 2080 | 0.7694503 | 0.8031888 | a |
| 27 | 3 | Hibrido | Algas Marinas 1,5 L/cil | 0.7229148 | 0.0086019 | 2080 | 0.7060455 | 0.7397840 | b |
| 29 | 6 | Hibrido | Algas Marinas 1,5 L/cil | 0.7204362 | 0.0086019 | 2080 | 0.7035669 | 0.7373055 | b |
| 30 | 0 | Hibrido | Algas Marinas 1,5 L/cil | 0.5982243 | 0.0086019 | 2080 | 0.5813550 | 0.6150936 | c |
| 36 | 12 | Hibrido | Azufre 100 gr.200 L-1 | 0.7332386 | 0.0086019 | 2080 | 0.7163693 | 0.7501078 | a |
| 38 | 9 | Hibrido | Azufre 100 gr.200 L-1 | 0.7314529 | 0.0086019 | 2080 | 0.7145836 | 0.7483221 | a |
| 37 | 3 | Hibrido | Azufre 100 gr.200 L-1 | 0.6680481 | 0.0086019 | 2080 | 0.6511788 | 0.6849174 | b |
| 39 | 6 | Hibrido | Azufre 100 gr.200 L-1 | 0.6655695 | 0.0086019 | 2080 | 0.6487003 | 0.6824388 | b |
| 40 | 0 | Hibrido | Azufre 100 gr.200 L-1 | 0.5433576 | 0.0086019 | 2080 | 0.5264883 | 0.5602269 | c |
| 46 | 12 | Hibrido | Azufre 150 gr.200 L-1 | 0.7735052 | 0.0086019 | 2080 | 0.7566360 | 0.7903745 | a |
| 48 | 9 | Hibrido | Azufre 150 gr.200 L-1 | 0.7717195 | 0.0086019 | 2080 | 0.7548503 | 0.7885888 | a |
| 47 | 3 | Hibrido | Azufre 150 gr.200 L-1 | 0.7083148 | 0.0086019 | 2080 | 0.6914455 | 0.7251840 | b |
| 49 | 6 | Hibrido | Azufre 150 gr.200 L-1 | 0.7058362 | 0.0086019 | 2080 | 0.6889669 | 0.7227055 | b |
| 50 | 0 | Hibrido | Azufre 150 gr.200 L-1 | 0.5836243 | 0.0086019 | 2080 | 0.5667550 | 0.6004936 | c |
| 56 | 12 | Hibrido | Suero de leche 10% | 0.7615719 | 0.0086019 | 2080 | 0.7447026 | 0.7784412 | a |
| 58 | 9 | Hibrido | Suero de leche 10% | 0.7597862 | 0.0086019 | 2080 | 0.7429169 | 0.7766555 | a |
| 57 | 3 | Hibrido | Suero de leche 10% | 0.6963814 | 0.0086019 | 2080 | 0.6795122 | 0.7132507 | b |
| 59 | 6 | Hibrido | Suero de leche 10% | 0.6939029 | 0.0086019 | 2080 | 0.6770336 | 0.7107721 | b |
| 60 | 0 | Hibrido | Suero de leche 10% | 0.5716910 | 0.0086019 | 2080 | 0.5548217 | 0.5885602 | c |
| 66 | 12 | Hibrido | Suero de leche 30% | 0.7741052 | 0.0086019 | 2080 | 0.7572360 | 0.7909745 | a |
| 68 | 9 | Hibrido | Suero de leche 30% | 0.7723195 | 0.0086019 | 2080 | 0.7554503 | 0.7891888 | a |
| 67 | 3 | Hibrido | Suero de leche 30% | 0.7089148 | 0.0086019 | 2080 | 0.6920455 | 0.7257840 | b |
| 69 | 6 | Hibrido | Suero de leche 30% | 0.7064362 | 0.0086019 | 2080 | 0.6895669 | 0.7233055 | b |
| 70 | 0 | Hibrido | Suero de leche 30% | 0.5842243 | 0.0086019 | 2080 | 0.5673550 | 0.6010936 | c |
p1a <- mc %>%
plot_smr(type = "line"
, x = "tiempo"
, y = "emmean"
, group = "variedad"
, sig = "group"
, error = "SE"
, color = T
, ylab = "Peso (g)"
, xlab = "Tiempo (h)"
, ylimits = c(0, 1, 0.2)
) +
facet_wrap(. ~ tratamiento, ncol = 2)
p1a
5.1.2 Porcentaje de Germination
trait <- "pg"
fb <- germination
lmm <- paste({{trait}}, "~ 1 + (1|bloque) + tratamiento*variedad") %>% as.formula()
lmd <- paste({{trait}}, "~ bloque + tratamiento*variedad") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## [1] index bloque tratamiento variedad pg resi
## [7] res_MAD rawp.BHStud adjp bholm out_flag
## <0 rows> (o 0- extensión row.names)
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: pg
## Df Sum Sq Mean Sq F value Pr(>F)
## bloque 2 633.3 316.7 0.6222 0.544582
## tratamiento 6 7000.0 1166.7 2.2922 0.065673 .
## variedad 1 4609.5 4609.5 9.0565 0.005753 **
## tratamiento:variedad 6 6857.1 1142.9 2.2454 0.070466 .
## Residuals 26 13233.3 509.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ variedad|tratamiento) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| variedad | tratamiento | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | criollo | Agua Destilada | 86.66667 | 13.02529 | 26 | 59.8928044 | 113.44053 | a |
| 1 | Hibrido | Agua Destilada | 63.33333 | 13.02529 | 26 | 36.5594710 | 90.10720 | a |
| 3 | Hibrido | Algas Marinas 1 L/cil | 70.00000 | 13.02529 | 26 | 43.2261377 | 96.77386 | a |
| 4 | criollo | Algas Marinas 1 L/cil | 30.00000 | 13.02529 | 26 | 3.2261377 | 56.77386 | b |
| 5 | Hibrido | Algas Marinas 1,5 L/cil | 56.66667 | 13.02529 | 26 | 29.8928044 | 83.44053 | a |
| 6 | criollo | Algas Marinas 1,5 L/cil | 36.66667 | 13.02529 | 26 | 9.8928044 | 63.44053 | a |
| 7 | Hibrido | Azufre 100 gr.200 L-1 | 66.66667 | 13.02529 | 26 | 39.8928044 | 93.44053 | a |
| 8 | criollo | Azufre 100 gr.200 L-1 | 16.66667 | 13.02529 | 26 | -10.1071956 | 43.44053 | b |
| 9 | Hibrido | Azufre 150 gr.200 L-1 | 76.66667 | 13.02529 | 26 | 49.8928044 | 103.44053 | a |
| 10 | criollo | Azufre 150 gr.200 L-1 | 26.66667 | 13.02529 | 26 | -0.1071956 | 53.44053 | b |
| 11 | Hibrido | Suero de leche 10% | 70.00000 | 13.02529 | 26 | 43.2261377 | 96.77386 | a |
| 12 | criollo | Suero de leche 10% | 70.00000 | 13.02529 | 26 | 43.2261377 | 96.77386 | a |
| 13 | Hibrido | Suero de leche 30% | 43.33333 | 13.02529 | 26 | 16.5594710 | 70.10720 | a |
| 14 | criollo | Suero de leche 30% | 33.33333 | 13.02529 | 26 | 6.5594710 | 60.10720 | a |
p1b <- mc %>%
plot_smr(type = "bar"
, x = "tratamiento"
, y = "emmean"
, group = "variedad"
, sig = "group"
, error = "SE"
, color = T
, ylab = "Germinación ('%')"
, xlab = "Tratamientos"
, ylimits = c(0, 120, 20)
)
p1b
5.1.3 Velocidad de germinación
trait <- "vg"
fb <- germination
lmm <- paste({{trait}}, "~ 1 + (1|bloque) + tratamiento*variedad") %>% as.formula()
lmd <- paste({{trait}}, "~ bloque + tratamiento*variedad") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## index bloque tratamiento variedad vg resi res_MAD
## 7 7 1 Algas Marinas 1,5 L/cil criollo 1 -1.666667 -3.372454
## 34 34 1 Azufre 150 gr.200 L-1 Hibrido 5 1.888889 3.822114
## rawp.BHStud adjp bholm out_flag
## 7 0.0007450159 0.0007450159 0.030545650 OUTLIER
## 34 0.0001323123 0.0001323123 0.005557118 OUTLIER
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: vg
## Df Sum Sq Mean Sq F value Pr(>F)
## bloque 2 1.3321 0.66607 1.3664 0.274150
## tratamiento 6 8.6594 1.44323 2.9608 0.026214 *
## variedad 1 2.0003 2.00025 4.1035 0.054051 .
## tratamiento:variedad 6 11.5622 1.92703 3.9533 0.006872 **
## Residuals 24 11.6989 0.48745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ variedad|tratamiento) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| variedad | tratamiento | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | criollo | Agua Destilada | 2.888889 | 0.4030931 | 24 | 2.0569455 | 3.720832 | a |
| 1 | Hibrido | Agua Destilada | 2.333333 | 0.4030931 | 24 | 1.5013900 | 3.165277 | a |
| 3 | Hibrido | Algas Marinas 1 L/cil | 3.277778 | 0.4030931 | 24 | 2.4458344 | 4.109721 | a |
| 4 | criollo | Algas Marinas 1 L/cil | 1.500000 | 0.4030931 | 24 | 0.6680567 | 2.331943 | b |
| 6 | criollo | Algas Marinas 1,5 L/cil | 3.379630 | 0.5004960 | 24 | 2.3466566 | 4.412603 | a |
| 5 | Hibrido | Algas Marinas 1,5 L/cil | 2.833333 | 0.4030931 | 24 | 2.0013900 | 3.665277 | a |
| 7 | Hibrido | Azufre 100 gr.200 L-1 | 3.833333 | 0.4030931 | 24 | 3.0013900 | 4.665277 | a |
| 8 | criollo | Azufre 100 gr.200 L-1 | 1.666667 | 0.4030931 | 24 | 0.8347233 | 2.498610 | b |
| 9 | Hibrido | Azufre 150 gr.200 L-1 | 2.046296 | 0.5004960 | 24 | 1.0133232 | 3.079269 | a |
| 10 | criollo | Azufre 150 gr.200 L-1 | 1.333333 | 0.4030931 | 24 | 0.5013900 | 2.165277 | a |
| 12 | criollo | Suero de leche 10% | 3.055556 | 0.4030931 | 24 | 2.2236122 | 3.887499 | a |
| 11 | Hibrido | Suero de leche 10% | 3.000000 | 0.4030931 | 24 | 2.1680567 | 3.831943 | a |
| 14 | criollo | Suero de leche 30% | 2.333333 | 0.4030931 | 24 | 1.5013900 | 3.165277 | a |
| 13 | Hibrido | Suero de leche 30% | 1.833333 | 0.4030931 | 24 | 1.0013900 | 2.665277 | a |
p1c <- mc %>%
plot_smr(type = "bar"
, x = "tratamiento"
, y = "emmean"
, group = "variedad"
, sig = "group"
, error = "SE"
, color = T
, ylab = "Velocidad de germinación (días)"
, xlab = "Tratamientos"
, ylimits = c(0, 6, 1)
)
p1c
5.1.4 Indice de germinación
trait <- "ig"
fb <- germination
lmm <- paste({{trait}}, "~ 1 + (1|bloque) + tratamiento*variedad") %>% as.formula()
lmd <- paste({{trait}}, "~ bloque + tratamiento*variedad") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## index bloque tratamiento variedad ig resi res_MAD
## 25 25 1 Algas Marinas 1 L/cil Hibrido 0.2 -1.466667 -3.29751
## rawp.BHStud adjp bholm out_flag
## 25 0.0009754607 0.0009754607 0.04096935 OUTLIER
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: ig
## Df Sum Sq Mean Sq F value Pr(>F)
## bloque 2 0.3050 0.1525 0.4149 0.664896
## tratamiento 6 10.3540 1.7257 4.6949 0.002507 **
## variedad 1 3.8850 3.8850 10.5697 0.003278 **
## tratamiento:variedad 6 6.5489 1.0915 2.9695 0.024965 *
## Residuals 25 9.1890 0.3676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ variedad|tratamiento) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| variedad | tratamiento | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | criollo | Agua Destilada | 2.6000000 | 0.3500293 | 25 | 1.8791012 | 3.3208988 | a |
| 1 | Hibrido | Agua Destilada | 1.7666667 | 0.3500293 | 25 | 1.0457678 | 2.4875655 | a |
| 3 | Hibrido | Algas Marinas 1 L/cil | 2.3679487 | 0.4341579 | 25 | 1.4737838 | 3.2621137 | a |
| 4 | criollo | Algas Marinas 1 L/cil | 0.6000000 | 0.3500293 | 25 | -0.1208988 | 1.3208988 | b |
| 5 | Hibrido | Algas Marinas 1,5 L/cil | 1.1333333 | 0.3500293 | 25 | 0.4124345 | 1.8542322 | a |
| 6 | criollo | Algas Marinas 1,5 L/cil | 0.5666667 | 0.3500293 | 25 | -0.1542322 | 1.2875655 | a |
| 7 | Hibrido | Azufre 100 gr.200 L-1 | 1.2333333 | 0.3500293 | 25 | 0.5124345 | 1.9542322 | a |
| 8 | criollo | Azufre 100 gr.200 L-1 | 0.1666667 | 0.3500293 | 25 | -0.5542322 | 0.8875655 | b |
| 9 | Hibrido | Azufre 150 gr.200 L-1 | 1.9666667 | 0.3500293 | 25 | 1.2457678 | 2.6875655 | a |
| 10 | criollo | Azufre 150 gr.200 L-1 | 0.5333333 | 0.3500293 | 25 | -0.1875655 | 1.2542322 | b |
| 11 | Hibrido | Suero de leche 10% | 1.7000000 | 0.3500293 | 25 | 0.9791012 | 2.4208988 | a |
| 12 | criollo | Suero de leche 10% | 1.6666667 | 0.3500293 | 25 | 0.9457678 | 2.3875655 | a |
| 13 | Hibrido | Suero de leche 30% | 1.0666667 | 0.3500293 | 25 | 0.3457678 | 1.7875655 | a |
| 14 | criollo | Suero de leche 30% | 0.5333333 | 0.3500293 | 25 | -0.1875655 | 1.2542322 | a |
p1d <- mc %>%
plot_smr(type = "bar"
, x = "tratamiento"
, y = "emmean"
, group = "variedad"
, sig = "group"
, error = "SE"
, color = T
, ylab = "Indice de germinación"
, xlab = "Tratamientos"
, ylimits = c(0, 5, 1)
)
p1d
5.2 Figura 1
legend <- cowplot::get_plot_component(p1b, 'guide-box-top', return_all = TRUE)
p1i <- list(p1b + labs(x = NULL) + theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
, p1c + labs(x = NULL) + theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
, p1d + labs(x = NULL) + theme(legend.position="none")
) %>%
plot_grid(plotlist = ., ncol = 1
, labels = c("b", "c", "d")
)
p1il <- list(legend, p1i) %>%
plot_grid(plotlist = ., ncol = 1, align = 'v', rel_heights = c(0.05, 1))
list(p1a, p1il) %>%
plot_grid(plotlist = ., ncol = 2, rel_widths = c(1.2, 1.8), labels = c("a")) %>%
ggsave2(plot = ., "files/Fig-1.jpg"
, units = "cm"
, width = 40
, height = 25
)
knitr::include_graphics("files/Fig-1.jpg")